U.S. patent application number 16/110759 was filed with the patent office on 2019-02-28 for system and method for rich conversation in artificial intelligence.
This patent application is currently assigned to CHIRRP, INC.. The applicant listed for this patent is CHIRRP, INC.. Invention is credited to Mallesh Murugesan, Xianfeng Yuan.
Application Number | 20190065498 16/110759 |
Document ID | / |
Family ID | 65435202 |
Filed Date | 2019-02-28 |
United States Patent
Application |
20190065498 |
Kind Code |
A1 |
Yuan; Xianfeng ; et
al. |
February 28, 2019 |
SYSTEM AND METHOD FOR RICH CONVERSATION IN ARTIFICIAL
INTELLIGENCE
Abstract
A method and system can include for "Rich Converstation" can
include receiving a search query, identifying an intent of the
search query, parsing the search query to identify one or more of
an entity identifier and a scope identifier where an entity
identifier is a subject of the search query and the scope
identifier is a scope definition associated with the search query,
identifying an answer to the search query based upon a user profile
and the scope definition, generating a conversation-based
interaction using the scope definition, and modifying the scope
definition using the conversation-based interaction and user
profile. The method and system can further modify a scope
definition for a future conversation-based interaction based upon a
prior conversation-based interaction and the user profile and
present the answer to the search query and a second answer based on
the future conversation-based interaction.
Inventors: |
Yuan; Xianfeng; (Falls
Church, VA) ; Murugesan; Mallesh; (Miami,
FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CHIRRP, INC. |
Miami |
FL |
US |
|
|
Assignee: |
CHIRRP, INC.
Miami
FL
|
Family ID: |
65435202 |
Appl. No.: |
16/110759 |
Filed: |
August 23, 2018 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62551280 |
Aug 29, 2017 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/90332 20190101;
G06F 16/248 20190101; G06F 16/24522 20190101; G06N 5/04 20130101;
G06N 5/027 20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06N 5/02 20060101 G06N005/02 |
Claims
1. One or more computer-storage media having computer-executable
instructions embodied thereon that, when executed by one or more
computing devices, perform a method, the method comprising:
receiving, via a user input coupled to the one or more computing
devices, a search query in a query-based interaction; parsing, by
the one or more computing devices, the search query to identify one
or more of an entity identifier and a scope identifier, wherein an
entity identifier is a subject of the search query and the scope
identifier is a scope definition associated with the search query;
identifying, by the one or more computing devices, an answer to the
search query based upon a user profile and the scope identifier;
generating, by the one or more computing devices, a
conversation-based interaction using the scope definition;
modifying, by the one or more computing devices, the scope
definition using the conversation-based interaction and user
profile; modifying, by the one or more computing devices, a scope
definition for a future conversation-based interaction based upon a
prior conversation-based interaction; presenting, via a user output
device coupled to the one or more computing devices, the answer to
the search query and a second answer based on the future
conversation-based interaction.
2. The media of claim 1, wherein the search query is a text input
or a voice input.
3. The media of claim 1, wherein the answer is displayed in
combination with one or more web search results or in combination
with an artificial intelligence based framework.
4. The media of claim 1, wherein the scope definition persists
among and between query-based interactions and conversation-based
interaction until an intention identifying module determines that
the scope definition has changed based on a defined set of exit
criteria.
5. The media of claim 1, further comprising maintaining user level
universal variables across different query based interactions and
conversation based interactions.
6. A computerized method, the method comprising: receiving via a
user input coupled to one or more computing devices a search query;
identifying by the one or more computing devices an intent of the
search query; parsing by the one or more computing devices the
search query to identify one or more of an entity identifier and a
scope identifier, wherein an entity identifier is a subject of the
search query within a scope definition associated with the search
query and scope identifier; identifying by the one or more
computing devices an answer to the search query based upon a user
profile and the scope definition; generating by the one or more
computing devices a conversation-based interaction using the scope
definition; modifying by the one or more computing devices the
scope definition using the conversation-based interaction and user
profile; modifying by the one or more computing devices a scope
definition for a future conversation-based interaction based upon a
prior conversation-based interaction and the user profile;
presenting via a user output device coupled to the one or more
computing devices the answer to the search query and a second
answer based on the future conversation-based interaction.
7. The method of claim 6, wherein the method stores a user-level
universal variable in a user profile and further stores a universal
context variable for the user.
8. The method of claim 6, wherein the method maintains a scope
definition within a query based interaction or a conversation based
interaction until a defined exit criteria is met.
9. The method of claim 6, wherein query based interactions and
conversation based interactions are linked.
10. A system, comprising: a memory having computer instructions
stored therein; one or more processors coupled to the memory,
wherein the one or more processors upon execution of the computer
instructions cause the one or more processors to perform the
operations comprising: receiving a search query; identifying an
intent of the search query; parsing the search query to identify
one or more of an entity identifier and a scope identifier, wherein
an entity identifier is a subject of the search query and the scope
identifier is a scope definition associated with the search query;
identifying an answer to the search query based upon a user profile
and the scope definition; generating a conversation-based
interaction using the scope definition; modifying the scope
definition using the conversation-based interaction and user
profile; modifying a scope definition for a future
conversation-based interaction based upon a prior
conversation-based interaction and the user profile; presenting the
answer to the search query and a second answer based on the future
conversation-based interaction.
11. The system of claim 10, wherein a current scope of a
conversation based interaction is modified based on a universal
user variable and a session variable stored in the memory or stored
in a second memory.
12. The system of claim 10, wherein the system comprises an
intention identifying module, a dialog based module, a query based
module, a linking module, and one or more backend databases.
13. The system of claim 12, wherein the intention identifying
module is configured to handle user responses and questions each
time the system receives a user query.
14. The system of claim 12, wherein the dialog based module is
configured to identify the current scope based upon defined exit
criteria and a single previous response received by a user or based
upon several previous dialogues or conversations with the user.
15. The system of claim 12, wherein the intention identifying
module uses a natural language understatnding module to determine
if a conversation is remaining within a current scope, if an exit
criteria has been met, or if a response to a query is looking for a
different scope outside the current scope.
16. The system of claim 10, wherein the one or more processors are
coupled to an artificial intelleigence engine including a
speech-to-text engine or a text-to-speech engine.
17. The system of claim 10, wherein the one or more processors are
coupled to a front-end input engine coupled to a social media
messaging application, an Internet video-conferencing application,
a stand-alone internet voice processing search engine, a
voice-to-chat interface, or an voice to instant messaging
application.
18. The system of claim 10, wherein the system is coupled to an
enterprise database which is used to control the scope definition,
or is coupled to public sources of information to enhance user
profile variables.
19. The system of claim 10, wherein the system is configured to
redirect a conversation to a live attendant based on rule based
criteria.
20. The system of claim 10, wherein the system is configure to
identify the intent of the query through a natural language
understanding processor.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority under 35 U.S.C. Section
119(e) to U.S. Provisional Application No. 62/551,280 filed on Aug.
29, 2017, the entire content of which is incorporated herein by
reference.
FIELD OF THE DISCLOSURE
[0002] The present disclosure generally relates to systems and
methods for providing artificial intelligence, and more
particularly relates to an innovative system and related method to
render Human-like conversations by an artificial intelligence
engine/agent by incorporating specific methodologies to improve and
enhance the accuracy of user intents and user conversations by
putting intention of the user into controlled scope and providing
better accuracy in identifying intention.
BACKGROUND
[0003] In artificial intelligence, there are several components
that make a machine knowledgeable to be able to respond to user
requests as data. A first component is understanding the context
and the knowledge base of that data. Once the machine learns and
understands the data and creates context and insights from a
collection of documents and data, it can answer questions
intelligently on that data set. Most Artificial Intelligence (AI)
agents, use machine learning algorithms to detect "signals" or
patterns in the data. Users can load their data and document
collection into the service, train a machine learning model based
on known relevant results, then leverage this model to provide
improved results (generally known as "Retrieve and Rank" to their
end users based on their question or query (Ex: an experienced
technician can quickly find solutions from dense product manuals).
We will refer to this as "query based interaction". In short, query
based interaction is where the user asks a question and the system
responds with the relevant results based on machine learning.
[0004] The second component to providing relevant responses and
meaningful dialog with the user is through structured questions. In
this model, a structured question and answer model is created that
will take the user thru a standard set of questions to a final
decision point to provide the best possible personalized answer to
the user. This type of conversation based interaction is where the
system asks questions to the user to understand the intent of the
user further based on a specific scenario (commonly known as
"Conversation"). We will refer to this as "conversation based
interaction."
BRIEF DESCRIPTION OF DRAWINGS
[0005] FIG. 1 is a flow chart of a method of rich conversation in
accordance with an embodiment;
[0006] FIG. 2 is a high level structure block diagram of a system
using the method of FIG. 1 in accordance with an embodiment;
[0007] FIG. 3 is a flow chart of a method of controlled scoping of
interactions in accordance with an embodiment;
[0008] FIG. 4 is a block diagram of a system illustrating
interactions among query-based interaction modules and
conversation-based interaction module in accordance with an
embodiment;
[0009] FIG. 5 is a block diagram illustrating a flow of controlled
scoping of interactions in accordance with an embodiment;
[0010] FIG. 6 illustrates a system for rich conversations in
accordance with an embodiment.
DETAILED DESCRIPTION
[0011] In the current state of the art, artificial intelligence
conversations are very basic and do not have the robust nature of
human conversations. This is because of several reasons:
[0012] 1) Cognitive conversation is not mature to handle robust
dialogs;
[0013] 2) Intent identification is a challenge in the cognitive
world;
[0014] 3) Knowledge about the user is limited to a single
conversation and does not transfer to other conversations; and
[0015] 4) History of user preferences, likes, etc. are not used in
conversation to provide more human like personalized
interaction.
[0016] There is a current need in the art for a system and related
method for providing rich conversations in artificial intelligence
that will provide solutions to the above list. It would be
desirable for such a system and related method to clearly define
query based interaction and conversation based interaction, thereby
making the building of each conversation easier. By having a smooth
transition between the components, a much richer conversation can
be built. A robust conversation methodology is of high need in the
artificial intelligence space as enterprises are moving towards AI
based customer service and engagement. In order for enterprises to
provide the best relevant customer service, much more robust
conversations and a high level of understanding of intents is
desired.
[0017] Where query based interactions and conversation based
interaction, as discussed above, operate independently and there is
no logical connection between the two, embodiments herein provide
the capability to start a conversation in query based component and
switch to conversation based component based on user queries and
have controlled scope to identify intents accurately. We call this
"rich conversation". This enables the user to have a more enhanced
and a more human like conversation with the cognitive system by
seamlessly switching between query based interaction to a
conversation based interaction and controlling the scope of the
conversation. This controlled scope of the conversation provides
further relevance and accuracy to the conversation. When we look at
human conversations, it is a combination of several things: 1.
Understanding who the user is, 2. Asking relevant questions 3.
Providing appropriate answers 4. Knowing the context of the
conversation and the current and past history of the conversation.
In order for machines to simulate human conversation, the above
mentioned points are critical and need to be incorporated into an
AI system. The present system and method of "rich conversation" is
the only platform that provides a solution that encapsulates the
above mentioned bullets (1 through 4). This is done by creating
controlled smaller scope, implementing user variable, seamlessly
transitioning between scopes, query based and Conversation based
interactions and using current and past history of the
conversation. These will be explained in detail below.
[0018] Rich conversation is focused on building human-like
conversation instead of just understanding human language.
[0019] In addition to combining query based and conversation based
interaction, rich conversation can understand user intents easily.
Intention recognition is a branch of artificial intelligence, it is
the process of a computer system becoming aware of the goals of one
or more users by observing and analyzing the queries. In Rich
conversation, there is a clear separation between multiple smaller
scope components of conversation and clear entry and exit criteria
between them. So when a user poses a question or provides a
response, the system assumes that the user response is within the
current scope until a clear entry/exit criteria is met. This
significantly improves accuracy of response, thereby improving user
experience. The system provides an intelligent persistence in
memory and scope to determine the appropriate context as the system
transitions between query based interactions and conversation based
interactions.
[0020] By creating structured questions, the user is taken thru a
standard set of questions and thereby the intent is more focused.
Conversation based interaction is built with the premise of having
workflow and scenario based conversations with the system leading
the user to a specific answer or call to action. But the
conversation module has its limitations. Conversation based
interaction is stateless (i.e. once a conversation has ended, these
variables will be lost). This existing system forces the system to
ask these questions again in order to know about the user.
[0021] Referring to FIG. 1, a flow chart is shown illustrating a
method 10 in accordance with the embodiments. In some embodiments,
the method 10 can begin with a step 11 of receiving a search query
or other form of query. At step 12, the method can identify an
intent of the query through, for example, natural language
understanding (NLU). The search query is then parsed and/or
analyzed at step 13 to analyze the intent of the query by
identifying one or more of an entity identifier and a scope
identifier or definition. The entity identifier can be the subject
of the search query within a scope definition associated with the
search query and scope identifier. At step 14, the method
identifies an answer to the search query based upon a user profile
and the scope definition. At decision block 19, if a confidence
level meets or exceeds a predetermined threshold that the answer is
correct, then the method can simply provide the answer to the query
at step 18A. If the confidence level fails to meet the
predetermined threshold level at decision block 19, then the method
can generate a conversation-based interaction using the scope
definition at step 15. Based on the responses the system receives
from a user from the conversation-based interaction, the scope
definition can be modified at step 16 based on the conversation
based interaction and/or a user profile. As will be further
explained below, universal variables and session variables can be
retained or can persist among different conversations (whether
query-based or conversation-based) such that context and scoping
can be appropriately modified (and likely improved from one session
to the next using artificial intelligence). Thus, the method can
modify a scope definition for a future conversation-based
interaction based upon a prior conversation-based interaction
and/or user profile at step 17. Assuming the user profile retains
or tracks the contexts of prior interactions, the user profile can
be a storage mechanism to improve future interactions. Otherwise,
other databases or storage mechanisms can be used to retain user
level variables, universal variables, session variables, and scope
definitions as appropriate from one interaction to the next for a
particular user or a set of users. At step 18, the method can
present the answer to a search query and additional or second
answers based on the future conversation-based interaction(s).
Thus, as previously stated, the system provides an intelligent
persistence in memory and scope to determine the appropriate
context as the system transitions between query based interactions
and conversation based interactions.
[0022] Referring to FIG. 2, a proposed "Chirrp" platform 200
handles a conversation based interaction using a "universal
variable" concept wherein when a user related question presented by
a user 201 is answered, the answer is stored in a user based
variable independent of where in the conversation the user is in.
This enables, this variable to be utilized within or outside that
specific conversation in the future. These variables are universal
and can be utilized in any conversation on a per needed basis. This
ability to go between conversations and retain user level variables
and information is unique. As an example: In a health related
conversation, if the users answers that their age range is in the
50's, that information is used in other areas (or other
conversations) to provide questions related to that age range. This
scenario helps with streamlining the conversation and making it
more relevant to the user.
[0023] Such a system can include a service layer 202 that
interfaces with at least a user level database 203 that can
maintain a user profile for example. The service layer 202 can
further interface with a generic content repository 204 that is
further coupled to other sources of data such as enterprise
documents 207, internal data repositories 209, and/or third party
or external data repositories 211. The context and scoping of the
conversations can also be maintained via an interface between the
service layer 202 and a conversation repository 205.
[0024] Key features of the system of such a system in accordance
with the embodiments can include:
[0025] a) Separation of query based and dialog based
conversation
[0026] b) Smooth transition between the components by using exit
and entry criteria.
[0027] c) Accurate identification of intent thru smaller scope
[0028] d) Knowledge about the user
[0029] e) user-level variables
[0030] f) session variables
[0031] g) scope level (content) variables
[0032] h) universal variables
[0033] The embodiments disclosed are unique in that the structure
and the utilization of the APIs are done in a way to provide better
conversations with the user. Although the technologies individually
exist, the creation of the structure and the methodology is
unique.
[0034] Referring to FIG. 3, a system 300 in accordance with the
embodiments can have a user 301 initiate query-based interaction
where a user variable, a session variable, and a current scope is
maintained at a module 302. An intention identifying module 303
that can provide a scope definition can determine if a current user
query is within a current user scope at 304, whether the scope is
not the current user scope at 306 ("what is the current scope"),
and whether the intention is to exit the current scope at 308. The
current scope database 309 tracks and provides the appropriate
information based on generic content 310 or other defined scopes
(312, 314, 316, or 318) as the method progresses and refines the
scope definition through the various interactions herein.
[0035] In AI, the knowledge base is looked at as a large
encyclopedia and user queries are analyzed to provide the right
answer independent from each other. This makes understanding of the
user intents very hard.
[0036] Another concept in current AI technology is taking the user
through a specific set of questions without additional variable
information to get to a final result. Ex: ordering flowers/pizza
etc. This scenario does not provide for any exit scenarios into
other conversation and so makes user experiences hard. It forces
the user to complete a full conversation before moving to the next
step in conversation.
[0037] In Rich conversation, we look at the entire conversation
with the user as a combination of multiple small and medium scoped
queries and conversations and provide intelligent ways to move
between these conversations. This technique will create additional
smaller interaction modules than the typical AI conversation
module. This enables identification and accuracy of intents.
[0038] Rich Conversation System.
[0039] A present embodiment can be a rich conversation system 400
as illustrated in FIG. 4, an embodiment of which is made up of the
following components: an intention identifying module 404; a
small-scope or controlled-scope, dialog-based module 405; a
small-scope or controlled-scope, query-based module 403; a linking
or controller module 406; and one or more backend databases 407. In
system embodiments, the modules and the database(s) are operatively
in communication and the modules connect to the database to
retrieve user level information including but not limited to
profile information and previous conversation information.
[0040] The intention identifying module 404 handles the all the
user responses and questions each time a user starts a
conversation.
[0041] The implementation of small scope of conversation is
different than usual identifying entities methodologies used in
regular searches. The conversation scope is identified by the
current controlled scope the user is in, which could be a single
previous response or several previous dialogs or conversations.
[0042] The user responses are passed through (Natural Language
Understanding) NLU 409 (which can exist as an independent module or
be part of one or more of the intention identifying module 404,
linking or controller modules 406, or other aforementioned modules)
to derive the meaning of the responses before scope of conversation
is determined.
[0043] Each response from the user is checked for one of the 3
following critierias:
[0044] a) If conversation is in one scope, it will stay in the
scope until the exit criteria is met
[0045] b) Is there an exit criteria (ex: Quit, stop etc)
[0046] c) Is the response looking for a completely new scope.
[0047] The small-scope, dialog-based module 405 handles dialog
based conversation in small scope.
[0048] This module will have a well-defined set of exit criteria,
including but not limited to: [0049] a) All system based questions
are answered [0050] b) Session expired [0051] c) User triggered
exit; or [0052] d) Rating from a common NLU service can be used for
exiting scope. The system can send the input from the user to both
small scope and large scope of the common NLU service at the same
time from the dialog based module 405, and the common NLU service
would return the rating of response from both NLU services. If the
Intention Identifier shows that the user has a lot higher rate on
larger scope than the current scope, the system exits the current
scope.
[0053] The small-scope, query-based module 403 handles query based
conversation in small scope. This module will have a well-defined
set of exit criteria, including but not limited to: [0054] a)
Rating from a common NLU service can also be an important factor in
the determination of exiting scope for the query based module 403.
The system can send the input from the user to both small scope and
large scope of the common NLU service of the query based module 403
at the same time, and the common NLU service would return the
rating of response from both NLU services. If the Intention
Identifier shows that user has a lot higher rate on larger scope
than the current scope, then the system exits the current scope.
[0055] b) Session expired [0056] c) User uses a keyword to exit
(Ex: Quit, Stop etc.)
[0057] The linking or controller module 406 links between the
different types of modules that establish relationship between
various components of the rich conversation including but not
limited to query based and conversation based interactions, small
scope components, database calls for user information etc.
[0058] The one or more backend databases support, for example, user
information and conversation history.
[0059] Further embodiments may be augmented by utilizing multiple
external APIs or other AI frameworks 408 such as API.AI, IBM Watson
APIs. For example, a Speech to Text and Text to Speech AI engine
will allow the user to have a conversation through voice. This
makes the rich conversation more powerful as the voice based
conversation mimics human conversation very closely. Another
embodiment contemplates working with additional AI based
technologies to enhance the context of data and create intelligence
from the data. Yet another embodiment contemplates a front-end user
interface 401 (via multi-channel or generic APIs 402 as required)
that is a component of rendering these rich conversations to the
user. Multiple channels can be used, including but not limited to,
Facebook Messenger, Skype, Slack, Amazon Alexa, Native app, or a
Web interface.
[0060] Enterprise data is a component of rich conversation.
Embodiments may also integrate with enterprise data to provide
answers to user queries. Enterprise data will be consumed and
controlled scope will be created from that data. Embodiments of the
rich conversation system may also integrate with external APIs to
enhance the capabilities of the conversation.
[0061] Example: When a question "Where is the Lincoln Memorial?" is
asked, other AI technologies will look through its vast amounts of
data and answer the question that has the highest confidence level.
Rich conversation will look through its data and find the "Lincoln
Memorial" scope and will answer it from within that scope. So, when
a follow up question like "What time does it open?" is asked, other
AI technologies will not know what "it" is associated with. With
Rich conversation, since the scope is Lincoln Memorial, rich
conversation will be able to identify "it" to be "Lincoln
Memorial". If an additional question is asked "What street is it
on?", rich conversation will still be able to answer it accurately
as the scope is still in Lincoln Memorial. This scope will be kept
until an exit criterion is met at which point, the user will be
taken to another scope.
[0062] Another feature that makes rich conversation unique, is
identifying entry points into the conversation as illustrated in
the scoping chart 500 of FIG. 5. When a question "What are the best
tours in DC?" is asked, instead of providing a list of tours, rich
conversation linking module will identify it to be an entry
criteria into a control scoped conversation and will take the user
into a controlled scope conversation and so will kick it off by
responding with a conversational query Ex: "What would you like to
see?" If the response is science museums, then the system may
respond with relevant and appropriate information that fits within
a new controlled scope such as the Air & Space Museum content
scope 512 instead of generic content for DC from repository 503 or
other unrelated content based on neighborhoods (516-526) or based
on things to do (528-536). (Unrelated content based on
neighborhoods with various scopes can include for example
neighborhood generic content 518 or neighborhood content for
specific neighborhoods such as Adams Morgan 520, Dupont Circle 522,
Arlington 524 or Bethesda 526. Unrelated content with various
scopes based on "Things to do" can include for example Things to do
generic content 529, or Sports 530, or Nightlife 532, or DC Tours
534, or Attractions 536. The system can also include a weighting
algorithm or a cross-referencing system that will ultimately still
lead to the appropriate Air & Space museum content scope (for
example, the DC Tours 534 content under the Things to Do Scope 528
can be cross referenced to scope 512). Based on recognition of key
words and context such as "science museums" and "day tour", the
appropriate scoping through Museums 504 and the air & space
museum 512 will be provided instead of other generic museum content
506 or other unrelated museum content (508 related to the Lincoln
Memorial Museum, 510 related to the National Museum of Natural
History, or 514 related to the Hirshhorn Museum and Garden)
unrelated to the current scope. Within the current scoping of the
Air & Space Museum content scope 512, the system can then
provide Air & Space museum generic content 513 or provide an
Air & Space museum dialog 515 to further refine the scoping of
the information being provided to the user.
[0063] Further aspects of embodiments can include: [0064] a) having
the capability to consume enterprise data and create controlled
scope components from that data, [0065] b) consuming data from
other public sources (news, social etc) and enhance user profile
variables [0066] c) Ability to direct the call to a human if needed
on a rule based criteria.
[0067] Various embodiments of the present disclosure can be
implemented on an information processing system. The information
processing system is capable of implementing and/or performing any
of the functionality set forth above. Any suitably configured
processing system can be used as the information processing system
in embodiments of the present disclosure. The information
processing system is operational with numerous other general
purpose or special purpose computing system environments, networks,
or configurations. Examples of well-known computing systems,
environments, and/or configurations that may be suitable for use
with the information processing system include, but are not limited
to, personal computer systems, server computer systems, thin
clients, hand-held or laptop devices, multiprocessor systems,
mobile devices, microprocessor-based systems, set top boxes,
programmable consumer electronics, network PCs, minicomputer
systems, mainframe computer systems, Internet-enabled television,
and distributed cloud computing environments that include any of
the above systems or devices, and the like.
[0068] For example, a user with a mobile device may be in
communication with a server configured to implement the rich
conversation system, according to an embodiment of the present
disclosure. The mobile device can be, for example, a multi-modal
wireless communication device, such as a "smart" phone, configured
to store and execute mobile device applications ("apps"). Such a
wireless communication device communicates with a wireless voice or
data network using suitable wireless communications protocols. The
user signs in and access the rich conversation service layer,
including the various modules described above. The service layer in
turn communicates with various databases, such as a user level DB,
a generic content repository, and a conversation repository. The
generic content repository may, for example, contain enterprise
documents, internal data repositories, and 3.sup.rd party data
repositories. The service layer queries these databases and
presents responses back to the user based upon the rules and
interactions of the rich conversation modules.
[0069] The rich conversation system may include, inter alia,
various hardware components such as processing circuitry executing
modules that may be described in the general context of computer
system-executable instructions, such as program modules, being
executed by the system. Generally, program modules can include
routines, programs, objects, components, logic, data structures,
and so on that perform particular tasks or implement particular
abstract data types. The modules may be practiced in various
computing environments such as conventional and distributed cloud
computing environments where tasks are performed by remote
processing devices that are linked through a communications
network. In a distributed cloud computing environment, program
modules may be located in both local and remote computer system
storage media including memory storage devices. Program modules
generally carry out the functions and/or methodologies of
embodiments of the present disclosure, as described above.
[0070] In some embodiments, a system includes at least one memory
and at least one processor of a computer system communicatively
coupled to the at least one memory. The at least one processor can
be configured to perform a method including methods described
above.
[0071] According yet to another embodiment of the present
disclosure, a computer readable storage medium comprises computer
instructions which, responsive to being executed by one or more
processors, cause the one or more processors to perform operations
as described in the methods or systems above or elsewhere
herein.
[0072] As shown in FIG. 6, an information processing system 101 of
a system 100 can be communicatively coupled with the message data
analysis module 150 and a group of client or other devices, or
coupled to a presentation device for display at any location at a
terminal or server location. According to this example, at least
one processor 102, responsive to executing instructions 107,
performs operations to communicate with the data analysis module
150 via a bus architecture 208, as shown. The at least one
processor 102 is communicatively coupled with main memory 104,
persistent memory 106, and a computer readable medium 120. The
processor 102 is communicatively coupled with an Analysis &
Data Storage 115 that, according to various implementations, can
maintain stored information used by, for example, the message data
analysis module 150 and more generally used by the information
processing system 100. Optionally, this stored information can be
received from the client or other devices. For example, this stored
information can be received periodically from the client devices
and updated or processed over time in the Analysis & Data
Storage 115. Additionally, according to another example, a history
log can be maintained or stored in the Analysis & Data Storage
115 of the information processed over time. The message data
analysis module 150, and the information processing system 100, can
use the information from the history log such as in the analysis
process and in making decisions related to determining whether data
measured is considered an outlier or not.
[0073] The computer readable medium 120, according to the present
example, can be communicatively coupled with a reader/writer device
(not shown) that is communicatively coupled via the bus
architecture 208 with the at least one processor 102. The
instructions 107, which can include instructions, configuration
parameters, and data, may be stored in the computer readable medium
120, the main memory 104, the persistent memory 106, and in the
processor's internal memory such as cache memory and registers, as
shown.
[0074] The information processing system 100 includes a user
interface 110 that comprises a user output interface 112 and user
input interface 114. Examples of elements of the user output
interface 112 can include a display, a speaker, one or more
indicator lights, one or more transducers that generate audible
indicators, and a haptic signal generator. Examples of elements of
the user input interface 114 can include a keyboard, a keypad, a
mouse, a track pad, a touch pad, a microphone that receives audio
signals, a camera, a video camera, or a scanner that scans images.
The received audio signals or scanned images, for example, can be
converted to electronic digital representation and stored in
memory, and optionally can be used with corresponding voice or
image recognition software executed by the processor 102 to receive
user input data and commands, or to receive test data for
example.
[0075] A network interface device 116 is communicatively coupled
with the at least one processor 102 and provides a communication
interface for the information processing system 100 to communicate
via one or more networks 108. The networks 108 can include wired
and wireless networks, and can be any of local area networks, wide
area networks, or a combination of such networks. For example, wide
area networks including the internet and the web can
inter-communicate the information processing system 100 with other
one or more information processing systems that may be locally, or
remotely, located relative to the information processing system
100. It should be noted that mobile communications devices, such as
mobile phones, Smart phones, tablet computers, lap top computers,
and the like, which are capable of at least one of wired and/or
wireless communication, are also examples of information processing
systems within the scope of the present disclosure. The network
interface device 116 can provide a communication interface for the
information processing system 100 to access the at least one
database 117 according to various embodiments of the
disclosure.
[0076] The instructions 107, according to the present example, can
include instructions for monitoring, instructions for analyzing,
instructions for retrieving and sending information and related
configuration parameters and data. It should be noted that any
portion of the instructions 107 can be stored in a centralized
information processing system or can be stored in a distributed
information processing system, i.e., with portions of the system
distributed and communicatively coupled together over one or more
communication links or networks.
[0077] FIGS. 1-4 illustrate examples of methods or process flows,
according to various embodiments of the present disclosure, which
can operate in conjunction with the information processing system
100 of FIG. 6.
* * * * *